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Found 8 papers, 2 papers with code
Bidirectional Captioning for Clinically Accurate and Interpretable Models
Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks.
Sample-Specific Debiasing for Better Image-Text Models
Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval.
Using Multiple Instance Learning to Build Multimodal Representations
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e. g., image classification, visual grounding, and cross-modal retrieval.
RadTex: Learning Efficient Radiograph Representations from Text Reports
In this paper, we build a data-efficient learning framework that utilizes radiology reports to improve medical image classification performance with limited labeled data (fewer than 1000 examples).
Image Classification with Consistent Supporting Evidence
We define consistent evidence to be both compatible and sufficient with respect to model predictions.
Multimodal Representation Learning via Maximization of Local Mutual Information
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.
